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2010 | OriginalPaper | Chapter

46. Analysing Multiobjective Fitness Function with Finite State Automata

Author : Nada M. A. Al Salami

Published in: Machine Learning and Systems Engineering

Publisher: Springer Netherlands

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Abstract

This research analyses and discusses the use of Multiobjective fitness function to evolve Finite State Automata. Such automata can describe system’s behavior mathematically in an efficient manner. However system’s behavior must highly depend on its input-output specifications. Genetic Programming is used, and the fitness function is built to guide the evolutionary process in two different cases. First case: Single point fitness function is used where the only focus is on the correctness of the evolved automata. Second case: multiobjective fitness function is used since every real-world problem involves simultaneous optimization of several incommensurable and often competing objectives. Multiobjective optimization is defined as a problem of finding a Finite State Automata which satisfies: parsimony, efficiency, and correctness. It has been presented that for large and complex problems it is necessary to divide them into sub problem(s) and simultaneously breed both sub-program(s) and a calling program.

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previous chapter A Study of the Protein Folding Problem by a Simulation Model
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Metadata
Title
Analysing Multiobjective Fitness Function with Finite State Automata
Author
Nada M. A. Al Salami
Copyright Year
2010
Publisher
Springer Netherlands
Book
Machine Learning and Systems Engineering

Print ISBN: 978-90-481-9418-6

Electronic ISBN: 978-90-481-9419-3

Copyright Year: 2010

DOI
https://doi.org/10.1007/978-90-481-9419-3_46

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